Nonstationary Stochastic Resonance in a Reduced-Order Hodgkin-Huxley Neuron

In this work a physiologically realistic neural system model is shown to be able to detect a weak nonstationary signal through the addition of noise. It is shown that the signal transduction performance is optimised for a nonzero value of noise intenSity in a manner suggestive of stochastic resonance

A Stochastic Model of the Visual Evoked Response

A stochastic model for visual evoked response generation is proposed based on a compound neurological generator approach. Participation of individual generators is stochastically modelled in a physiologically realistic manner that captures the inherent variability in latencies and amplitudes associated with the component phases of the response. The model is invertible such that decomposition of real responses to reveal individual unit generator participation is possible and suggests that conventional averaging techniques may provide a truer picture of the visual evoked response than previously thought

A Model of the Median Sensory Nerve Compound Action Potential leading to a Method for Nerve Fibre Diameter Distribution

Nerve conduction studies (NCS) are one of the basic tools of the electrodiagnostic clinician and are performed in order to evaluate the integrity of peripheral nerve function . Using such techniques diagnosis of various diseases and disorders of nerve are possible. During NCS a peripheral nerve is stimulated using an electrical pulse of sufficient intensity to recruit as many nerve fibres as possible. This elicits a volley of action potentials (APs) in the individual nerve fibres which is then recorded at a distal point usually using surface electrodes. Usually only two components of the recorded response (called the compound action potential or CAP), the distal amplitude and the distal/peak latency are routinely recorded though the proximal latency is also used to construct an average measure of conduction velocity (CV). In this study a model of the sensory CAP of the median nerve as measured using bipolar electrodes is proposed and using this model an additional measure is extracted from the CAP. This measure represents the distribution of conduction velocities within the nerve trunk and from this a measure of nerve fibre diameter distribution can be ascertaine

A Stochastic Model of the Visual Evoked Response

A stochastic model for visual evoked response generation is proposed based on a compound neurological generator approach. Participation of individual generators is stochastically modelled in a physiologically realistic manner that captures the inherent variability in latencies and amplitudes associated with the component phases of the response. The model is invertible such that decomposition of real responses to reveal individual unit generator participation is possible and suggests that conventional averaging techniques may provide a truer picture of the visual evoked response than previously thought

An Oscillatory Neural Network Scheme for Temporal Encoding and Stimulus Recognition

A novel computational neuro-architecture based on the phase resetting properties of physiologically based neural oscillators is proposed. Analog input variables are encOded in the patterns of the firing times with individual recognition units operating as radial basis-functions

Nonstationary Stochastic Resonance in a Reduced-Order Hodgkin-Huxley Neuron

In this work a physiologically realistic neural system model is shown to be able to detect a weak nonstationary signal through the addition of noise. It is shown that the signal transduction performance is optimised for a nonzero value of noise intenSity in a manner suggestive of stochastic resonance

Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation

Rapid Serial Visual Presentation (RSVP) is a paradigm that supports the application of cortically coupled computer vision to rapid image search. In RSVP, images are presented to participants in a rapid serial sequence which can evoke Event-related Potentials (ERPs) detectable in their Electroencephalogram (EEG). The contemporary approach to this problem involves supervised spatial filtering techniques which are applied for the purposes of enhancing the discriminative information in the EEG data. In this paper we make two primary contributions to that field: 1) We propose a novel spatial filtering method which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we provide a comprehensive comparison of nine spatial filtering pipelines using three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern (CSP) and three linear classification methods Linear Discriminant Analysis (LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR). Three pipelines without spatial filtering are used as baseline comparison. The Area Under Curve (AUC) is used as an evaluation metric in this paper. The results reveal that MTWLB and xDAWN spatial filtering techniques enhance the classification performance of the pipeline but CSP does not. The results also support the conclusion that LR can be effective for RSVP based BCI if discriminative features are available

Scalable RDF Data Compression using X10

The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of Semantic Web applications that perform computations over large volumes of information. A typical method for alleviating the impact of this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding for this purpose is particularly prevalent in Semantic Web database systems. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we describe an encoding implementation based on the asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art MapReduce algorithm, we demonstrate a speedup of 2.6-7.4x and excellent scalability. These results illustrate the strong potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of larger scale Semantic Web applications

The Design And Clinical Use Of A Reflective Brachial Photoplethysmograph

This report concerns the design and clinical use of a reflective brachial photoplethysmograph. A plethysmograph is an instrument to obtain tracings showing volume changes of a part of the body. Originally this related to volume variations due to blood circulation within the body part of interest. The instrument is said to have been invented by Mosso of Turin around 1870 [1], known in Italian as a "pletismografo", and first reported in Scientific American in July 1872. A photoplethysmograph is an optical detector that indicates the volume of blood in or passing through an area of tissue. By placing the photoplethysmograph at or near the site of a human artery the pulse waveform can be detected and measured. The photoplethysmograph can be transmissive or reflective. There are a variety of sites around the body that are commonly used for detecting the pulse waveform including the finger, the ear lobe, and the foot. The device developed in this work is a reflective detector that uses the brachial artery as a photoplethysmographic site. There appear to be no prior indications in academic or patent literature of this site being used with this type of detector and consequently the authors believe this device to be novel and worthy of reporting to the research community